Welcome to the Voice Activity Detection (VAD) project utilizing Jupyter Notebooks. VAD plays a pivotal role in numerous audio processing applications by enabling the identification of segments in audio containing speech. This repository comprises Jupyter Notebooks that serve as practical demonstrations of VAD techniques and their practical applications.
Traditionally, VAD has relied on the empirical derivation of suitable filters, which often consumes a significant amount of time. However, in this project, we embrace a more efficient approach by harnessing the power of Machine Learning (ML) and hyperparameter optimization using tools such as Optuna. This modern approach yields substantial time savings, making VAD more accessible and practical.
One of the advantages of our approach is that ML models are designed to be highly generalizable. This means they can confidently capture a broader spectrum of datasets compared to narrowly tuned traditional filters. The result is a VAD system that can adapt to a wider range of audio sources and environments, enhancing its overall effectiveness.